Metaheuristic Optimization Algorithms and Civil Engineering

The purpose of the using metaheuristic algorithm is to obtain an efficient and quickly solution for the related problem from the solution space which consist of the all combination of design variables. Testing all combination to find best solution takes many computational times and calculation efforts when the number of design variables are high. To overcome this difficulty the metaheuristic optimization algorithms can be preferred. Metaheuristic algorithms don’t prefer to use complex mathematical explanation such as the differential equations. But they don’t promise to find the best solution always for a given problem.

The civil engineering problems can be as; optimal design of two or three dimensional structures, optimal design of geotechnical structures such as foundation structures and bases, optimum design of reinforced concrete structure or building elements, time-cost optimization for project work programs, optimization of reinforced concrete structures sensitive to environmental effects (CO₂ release) and so on.

Optimization in structural engineering can be defined as the most efficient use of design variables by determining an objective function such as total weight, cost, CO2 emission or work duration in a construction project where the project is to be carried out. Depending on the problem, the design variables can be as element dimensions, number of elements, material properties, geometry of construction, duration of business activity, or cost. It may become difficult to create a mathematical model for the calculation of the objective function by taking into account these design variables. The combination of all design variables must be tested on the mathematical model created to find the global solution of the problem. However, in the case of continuous design variables or large number of design variables, the calculation volume and the required calculation time is also increase. Therefore, the use of metaheuristic optimization algorithms is effective solution for the engineering problems. The use of materials is becoming more limited due to the decrease of material resources in every day. So, it is a necessary to design structures with less weight or volume with the help of the using optimization technique in the construction of the engineering structures. In addition to the material, an optimization process is also needed to create an effective work schedule depending on time and cost. In other words, it is uneconomic and a long time will be required to make the projecting process without any work schedule, material and cost optimization in all processes of the engineering structures from the design to construction.

Until now, the researchers developed many metaheuristic optimization algorithms by mimicking the natural phenomena, simulated the behavior of the animal’s life or imitating a classroom like TLBO [1]. The main purpose in the developing a new optimization algorithm is to find global optimal solution for an engineering problem in a short time by using the minimum number of function evaluation. Thus, the advances of the new developed algorithm is compared by using some performance criteria such as number of function evaluation, CPU time, number of run, mean and standard deviation of the value of fitness function. Some of the most popular metaheuristic optimizations are: Differential Evolution (DE) by Storm & Price [2], Simulated Annealing (SA) by Kirkpatrick [3], Big bang – big crunch (BBBC) Osman & Eksin [4], Ant Colony Optimization (ACO) by Dorigo [5], Particle Swarm Optimization (PSO) by Kennedy & Eberhart [6], Genetic Algorithms (GA) by Holland [7], Jaya by Rao [8], Harmony search (HS) by Geem et al. [9], Artificial Bee Colony (ABC) by Karaboğa & Bastürk [10], Tabu Search (TS) by Glover [11], Teaching–Learning-Based Optimization (TLBO) by Rao et al. [1], Charged System Search (CSS) by Kaveh & Talatahari [12] and Cultural Algorithm (CA) by Reynolds [13].

Using these metaheuristic algorithms different type of civil engineering problems are investigated by the researchers. In the sub-research areas such as construction management, structural analysis, hydraulic, material science, geotechnical engineering and transportation many studies have been presented to the open literature until now. Some examples for implementing of the metaheuristic algorithms related to the different sub-research areas in civil engineering can be given as following.

Buckling load optimization of laminated plates using TLBO [14] in mechanic, Jaya algorithm to design steel grillage structure [15], prediction of berm geometry using TLBO algorithm [16] in hydraulic, optimum design of tied back retaining wall using GA [17] in geotechnic, solving traffic signal coordination under oversaturation conditions using ACO [18] in transportation and multi-objective optimization for construction time-cost tradeoff problems using PSO in construction management [19-20].

1. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimisation: a novel method for constrained mechanical design optimisation problems. Comput Aided Design 43: 303-315.
2. Rainer Storn, Kenneth Price (1997) Differential evolution – a simple and efficient heuristic for global optimisation over continuous spaces. Journal of Global Optimization 11: 341-359.
3. Kirkpatrick S, Gelatt Jr CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220 (4598): 671-680.
4. Osman K. Erol, Ibrahim Eksin (2006) A new optimization method: Big Bang-Big Crunch. Advances in Engineering Software 37(2): 106–111.
5. DORIGO M (1992) Optimization, learning and natural algorithms. PhD Thesis, Dept. of Electronics, Politecnico di Milano, Italy.
6. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.
7. Holland JH (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
8. Venkata Rao (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International J of Industrial Eng Computations 7: 19–34.
9. Zong Woo Geem, Joong Hoon Kim, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation, 76(2): 60–68.
10. Dervis Karaboga, Bahriye Basturk (2007) A powerful and efficient algorithm for numerical function optimisation: artificial bee colony algorithm. Journal of Global Optimization 39(3): 459-471.
11. Fred Glover (1989) Tabu Search-Part I. ORSA Journal on Computing 1(3): 190-206.
12. Kaveh, Talatahari S (2010) A novel heuristic optimisation method: charged system search. Acta Mechanica 213(3-4): 267-289.
13. Reynolds RG (1994) An introduction to cultural algorithms evolutionary programming. Proc. 3rd Ann. Conf, World Scientific, River Edge, NJ, pp. 131-139.
14. Umut Topal, Trung Vo-Duy, Tayfun Dede, Ebrahim Nazarimofrad (2018) Buckling load optimization of laminated plates resting on Pasternak foundation using TLBO. Structural Engineering and Mechanics 67(6): 617-628.
15. Tayfun Dede (2018) Jaya Algorithm to Solve Single Objective Size Optimization Problem for Steel Grillage Structures. Steel and Composite Structures 26(2): 163-170.
16. Ergun Uzlu, Murat İhsan Kömürcü, Murat Kankal, Tayfun Dede, Hasan Tahsin Öztürk (2014) Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms. Applied Ocean Research 48: 103- 113.
17. Nabeel A Jasim, Ahmed M Al-Yaqoobi (2016) Optimum Design of Tied Back Retaining Wall. Open Journal of Civil Engineering 6: 139-155.
18. Putha R, Quadrifoglio L, Zechman E (2012) Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions. Computer-Aided Civil and Infrastructure Engineering 27(1): 14–28.
19. Hong Zhang, Heng Li (2010) Multi-objective particle swarm optimization for construction time-cost tradeoff problems. Construction Management and Economics 28(1): 75-88.
20. Xin-She Yang, Suash Deb (2009) Cuckoo search via levy flights. In Nature & Biologically Inspired Computing, NaBIC, India, 210–214.